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Practical TensorFlow.js = deep learn...

Rivera, Juan De Dios Santos.

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Practical TensorFlow.js = deep learning in web app development /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical TensorFlow.js/ by Juan De Dios Santos Rivera.
其他題名:	deep learning in web app development /
作者:	Rivera, Juan De Dios Santos.
出版者:	Berkeley, CA :Apress : : 2020.,
面頁冊數:	xxiv, 303 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Welcome to TensorFlow.js -- Chapter 2: Training Our First Models -- Chapter 3: Doing k-means with ml5.js -- Chapter 4: Recognizing Handwritten Digits with Convolutional Neural Networks -- Chapter 5: Making a Game with PoseNet, a Pose Estimator Model -- Chapter 6: Identifying Toxic Text from a Google Chrome Extension -- Chapter 7: Object Detection with a Model Trained in Google Cloud AutoML -- Chapter 8: Training an Image Classifier with Transfer Learning on Node.js -- Chapter 9: Time Series Forecasting and Text Generation with Recurrent Neural Networks -- Chapter 10: Generating Handwritten Digits with Generative Adversarial Networks -- Chapter 11: Things to Remember, What's Next for You, and Final Words -- Appendix A: Apache License 2.0.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-6273-3
ISBN:	9781484262733

Practical TensorFlow.js = deep learning in web app development /
Rivera, Juan De Dios Santos.

Practical TensorFlow.jsdeep learning in web app development /[electronic resource] :by Juan De Dios Santos Rivera. - Berkeley, CA :Apress :2020. - xxiv, 303 p. :ill., digital ;24 cm.

Chapter 1: Welcome to TensorFlow.js -- Chapter 2: Training Our First Models -- Chapter 3: Doing k-means with ml5.js -- Chapter 4: Recognizing Handwritten Digits with Convolutional Neural Networks -- Chapter 5: Making a Game with PoseNet, a Pose Estimator Model -- Chapter 6: Identifying Toxic Text from a Google Chrome Extension -- Chapter 7: Object Detection with a Model Trained in Google Cloud AutoML -- Chapter 8: Training an Image Classifier with Transfer Learning on Node.js -- Chapter 9: Time Series Forecasting and Text Generation with Recurrent Neural Networks -- Chapter 10: Generating Handwritten Digits with Generative Adversarial Networks -- Chapter 11: Things to Remember, What's Next for You, and Final Words -- Appendix A: Apache License 2.0.

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard, ml5js, tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. You will: Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis.

ISBN: 9781484262733

Standard No.: 10.1007/978-1-4842-6273-3doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .P73 2020

Dewey Class. No.: 006.3

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520 $a Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard, ml5js, tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. You will: Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis.
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